--- On Fri, 17/7/09, Etan Lakam wrote:
> I am analysis trial data from a randomized trial,
> comparing the the body mass index (bmi2) bewteen
> the grousp at follow-up adjusting for the baseline
> values of bmi (bmi1) Some of the values of bmi at
> baseline are missing and I was advised to use the
> missing indicator method to account for the
> missing values of bmi at baseline, thus to have a
> more efficient result. Being a movice in the field
> of missing data, I don not have a clue on how to
> do this is Stata.
That is a good thing, because you should not do it.
this is explained here:
http://www.stata.com/statalist/archive/2007-12/msg00030.html
If you really care about those observations and
your missing data process satisfies the MAR
assumption you could use -ice-. To install
-ice- type in Stata -findit ice- and follow
the links. (The MAR assumption means that the
probability of being missing does not depend
on the unobserved values, this assumption can
thus only be made plausible through a
theoretical argument and can never be
emprically tested)
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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